Solving a new mathematical model for the integrated cockpit crew pairing and rostering problem by meta-heuristic algorithms under the COVID-19 pandemic

Saemi, Saeed; Rashidi Komijan, Alireza; Tavakkoli-Moghaddam, Reza

doi:10.1080/01605682.2023.2253839

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Book1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Yang,

Gao,

Ding

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space.This article is categorized under: Technologies > Computational Intelligence Technologies > Artificial Intelligence

show abstract